Overview

Dataset statistics

Number of variables30
Number of observations7043
Missing cells5403
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 MiB
Average record size in memory1.1 KiB

Variable types

Text1
Categorical6
Boolean13
Numeric10

Alerts

Count has constant value "1"Constant
Quarter has constant value "Q3"Constant
Avg Monthly GB Download is highly overall correlated with Internet Service and 1 other fieldsHigh correlation
Avg Monthly Long Distance Charges is highly overall correlated with Phone Service and 1 other fieldsHigh correlation
Contract is highly overall correlated with OfferHigh correlation
Device Protection Plan is highly overall correlated with Monthly Charge and 1 other fieldsHigh correlation
Internet Service is highly overall correlated with Avg Monthly GB Download and 3 other fieldsHigh correlation
Internet Type is highly overall correlated with Internet Service and 1 other fieldsHigh correlation
Monthly Charge is highly overall correlated with Device Protection Plan and 10 other fieldsHigh correlation
Multiple Lines is highly overall correlated with Monthly ChargeHigh correlation
Number of Referrals is highly overall correlated with Referred a FriendHigh correlation
Offer is highly overall correlated with Contract and 3 other fieldsHigh correlation
Online Backup is highly overall correlated with Total ChargesHigh correlation
Phone Service is highly overall correlated with Avg Monthly Long Distance Charges and 1 other fieldsHigh correlation
Referred a Friend is highly overall correlated with Number of ReferralsHigh correlation
Streaming Movies is highly overall correlated with Monthly Charge and 3 other fieldsHigh correlation
Streaming Music is highly overall correlated with Monthly Charge and 1 other fieldsHigh correlation
Streaming TV is highly overall correlated with Monthly Charge and 2 other fieldsHigh correlation
Tenure in Months is highly overall correlated with Offer and 3 other fieldsHigh correlation
Total Charges is highly overall correlated with Device Protection Plan and 8 other fieldsHigh correlation
Total Long Distance Charges is highly overall correlated with Avg Monthly Long Distance Charges and 3 other fieldsHigh correlation
Total Revenue is highly overall correlated with Monthly Charge and 4 other fieldsHigh correlation
Unlimited Data is highly overall correlated with Avg Monthly GB Download and 2 other fieldsHigh correlation
Phone Service is highly imbalanced (54.1%)Imbalance
Offer has 3877 (55.0%) missing valuesMissing
Internet Type has 1526 (21.7%) missing valuesMissing
Customer ID has unique valuesUnique
Number of Referrals has 3821 (54.3%) zerosZeros
Avg Monthly Long Distance Charges has 682 (9.7%) zerosZeros
Avg Monthly GB Download has 1526 (21.7%) zerosZeros
Total Refunds has 6518 (92.5%) zerosZeros
Total Extra Data Charges has 6315 (89.7%) zerosZeros
Total Long Distance Charges has 682 (9.7%) zerosZeros

Reproduction

Analysis started2025-12-07 17:30:31.034477
Analysis finished2025-12-07 17:30:43.070131
Duration12.04 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Customer ID
Text

Unique 

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size405.9 KiB
2025-12-07T17:30:43.307132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row8779-QRDMV
2nd row7495-OOKFY
3rd row1658-BYGOY
4th row4598-XLKNJ
5th row4846-WHAFZ
ValueCountFrequency (%)
8779-qrdmv1
 
< 0.1%
7273-tefqd1
 
< 0.1%
4598-xlknj1
 
< 0.1%
4846-whafz1
 
< 0.1%
4412-yltkf1
 
< 0.1%
0390-dcfdq1
 
< 0.1%
3445-hxxgf1
 
< 0.1%
2656-fmokz1
 
< 0.1%
2070-fnexe1
 
< 0.1%
0094-oifmo1
 
< 0.1%
Other values (7033)7033
99.9%
2025-12-07T17:30:43.716133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)70430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Count
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size344.0 KiB
1
7043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17043
100.0%

Length

2025-12-07T17:30:43.847133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T17:30:43.941132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
17043
100.0%

Most occurring characters

ValueCountFrequency (%)
17043
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17043
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17043
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17043
100.0%

Quarter
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.9 KiB
Q3
7043 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14086
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ3
2nd rowQ3
3rd rowQ3
4th rowQ3
5th rowQ3

Common Values

ValueCountFrequency (%)
Q37043
100.0%

Length

2025-12-07T17:30:44.038133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T17:30:44.140133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
q37043
100.0%

Most occurring characters

ValueCountFrequency (%)
Q7043
50.0%
37043
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q7043
50.0%
37043
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q7043
50.0%
37043
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q7043
50.0%
37043
50.0%

Referred a Friend
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3821 
True
3222 
ValueCountFrequency (%)
False3821
54.3%
True3222
45.7%
2025-12-07T17:30:44.231173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Number of Referrals
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9518671
Minimum0
Maximum11
Zeros3821
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:44.346178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0011993
Coefficient of variation (CV)1.5376043
Kurtosis0.72196393
Mean1.9518671
Median Absolute Deviation (MAD)0
Skewness1.4460596
Sum13747
Variance9.0071972
MonotonicityNot monotonic
2025-12-07T17:30:44.460174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
03821
54.3%
11086
 
15.4%
5264
 
3.7%
3255
 
3.6%
7248
 
3.5%
9238
 
3.4%
2236
 
3.4%
4236
 
3.4%
10223
 
3.2%
6221
 
3.1%
Other values (2)215
 
3.1%
ValueCountFrequency (%)
03821
54.3%
11086
 
15.4%
2236
 
3.4%
3255
 
3.6%
4236
 
3.4%
5264
 
3.7%
6221
 
3.1%
7248
 
3.5%
8213
 
3.0%
9238
 
3.4%
ValueCountFrequency (%)
112
 
< 0.1%
10223
3.2%
9238
3.4%
8213
3.0%
7248
3.5%
6221
3.1%
5264
3.7%
4236
3.4%
3255
3.6%
2236
3.4%

Tenure in Months
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.386767
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:44.587174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.542061
Coefficient of variation (CV)0.75778052
Kurtosis-1.3870524
Mean32.386767
Median Absolute Deviation (MAD)22
Skewness0.24054261
Sum228100
Variance602.31276
MonotonicityNot monotonic
2025-12-07T17:30:44.731174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1613
 
8.7%
72362
 
5.1%
2238
 
3.4%
3200
 
2.8%
4176
 
2.5%
71170
 
2.4%
5133
 
1.9%
7131
 
1.9%
10127
 
1.8%
8123
 
1.7%
Other values (62)4770
67.7%
ValueCountFrequency (%)
1613
8.7%
2238
 
3.4%
3200
 
2.8%
4176
 
2.5%
5133
 
1.9%
6110
 
1.6%
7131
 
1.9%
8123
 
1.7%
9119
 
1.7%
10127
 
1.8%
ValueCountFrequency (%)
72362
5.1%
71170
2.4%
70119
 
1.7%
6995
 
1.3%
68100
 
1.4%
6798
 
1.4%
6689
 
1.3%
6576
 
1.1%
6480
 
1.1%
6372
 
1.0%

Offer
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.2%
Missing3877
Missing (%)55.0%
Memory size385.3 KiB
Offer B
824 
Offer E
805 
Offer D
602 
Offer A
520 
Offer C
415 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters22162
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffer E
2nd rowOffer D
3rd rowOffer C
4th rowOffer C
5th rowOffer C

Common Values

ValueCountFrequency (%)
Offer B824
 
11.7%
Offer E805
 
11.4%
Offer D602
 
8.5%
Offer A520
 
7.4%
Offer C415
 
5.9%
(Missing)3877
55.0%

Length

2025-12-07T17:30:44.878175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T17:30:45.000175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
offer3166
50.0%
b824
 
13.0%
e805
 
12.7%
d602
 
9.5%
a520
 
8.2%
c415
 
6.6%

Most occurring characters

ValueCountFrequency (%)
f6332
28.6%
O3166
14.3%
e3166
14.3%
r3166
14.3%
3166
14.3%
B824
 
3.7%
E805
 
3.6%
D602
 
2.7%
A520
 
2.3%
C415
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)22162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f6332
28.6%
O3166
14.3%
e3166
14.3%
r3166
14.3%
3166
14.3%
B824
 
3.7%
E805
 
3.6%
D602
 
2.7%
A520
 
2.3%
C415
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f6332
28.6%
O3166
14.3%
e3166
14.3%
r3166
14.3%
3166
14.3%
B824
 
3.7%
E805
 
3.6%
D602
 
2.7%
A520
 
2.3%
C415
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f6332
28.6%
O3166
14.3%
e3166
14.3%
r3166
14.3%
3166
14.3%
B824
 
3.7%
E805
 
3.6%
D602
 
2.7%
A520
 
2.3%
C415
 
1.9%

Phone Service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True6361
90.3%
False682
 
9.7%
2025-12-07T17:30:45.406210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Avg Monthly Long Distance Charges
Real number (ℝ)

High correlation  Zeros 

Distinct3584
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.958954
Minimum0
Maximum49.99
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:45.507067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.21
median22.89
Q336.395
95-th percentile47.34
Maximum49.99
Range49.99
Interquartile range (IQR)27.185

Descriptive statistics

Standard deviation15.448113
Coefficient of variation (CV)0.6728579
Kurtosis-1.2546544
Mean22.958954
Median Absolute Deviation (MAD)13.6
Skewness0.049175899
Sum161699.91
Variance238.64421
MonotonicityNot monotonic
2025-12-07T17:30:45.630184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0682
 
9.7%
18.267
 
0.1%
18.746
 
0.1%
30.096
 
0.1%
42.556
 
0.1%
45.926
 
0.1%
30.076
 
0.1%
25.576
 
0.1%
49.516
 
0.1%
18.426
 
0.1%
Other values (3574)6306
89.5%
ValueCountFrequency (%)
0682
9.7%
1.011
 
< 0.1%
1.023
 
< 0.1%
1.031
 
< 0.1%
1.051
 
< 0.1%
1.061
 
< 0.1%
1.071
 
< 0.1%
1.082
 
< 0.1%
1.092
 
< 0.1%
1.11
 
< 0.1%
ValueCountFrequency (%)
49.991
 
< 0.1%
49.983
< 0.1%
49.962
< 0.1%
49.952
< 0.1%
49.941
 
< 0.1%
49.921
 
< 0.1%
49.913
< 0.1%
49.93
< 0.1%
49.881
 
< 0.1%
49.871
 
< 0.1%

Multiple Lines
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4072 
True
2971 
ValueCountFrequency (%)
False4072
57.8%
True2971
42.2%
2025-12-07T17:30:45.729175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Internet Service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
5517 
False
1526 
ValueCountFrequency (%)
True5517
78.3%
False1526
 
21.7%
2025-12-07T17:30:45.809175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Internet Type
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing1526
Missing (%)21.7%
Memory size389.1 KiB
Fiber Optic
3035 
DSL
1652 
Cable
830 

Length

Max length11
Median length11
Mean length7.7018307
Min length3

Characters and Unicode

Total characters42491
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSL
2nd rowFiber Optic
3rd rowFiber Optic
4th rowFiber Optic
5th rowFiber Optic

Common Values

ValueCountFrequency (%)
Fiber Optic3035
43.1%
DSL1652
23.5%
Cable830
 
11.8%
(Missing)1526
21.7%

Length

2025-12-07T17:30:45.914182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T17:30:46.007182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
fiber3035
35.5%
optic3035
35.5%
dsl1652
19.3%
cable830
 
9.7%

Most occurring characters

ValueCountFrequency (%)
i6070
14.3%
b3865
9.1%
e3865
9.1%
F3035
7.1%
r3035
7.1%
3035
7.1%
O3035
7.1%
p3035
7.1%
t3035
7.1%
c3035
7.1%
Other values (6)7446
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)42491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i6070
14.3%
b3865
9.1%
e3865
9.1%
F3035
7.1%
r3035
7.1%
3035
7.1%
O3035
7.1%
p3035
7.1%
t3035
7.1%
c3035
7.1%
Other values (6)7446
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i6070
14.3%
b3865
9.1%
e3865
9.1%
F3035
7.1%
r3035
7.1%
3035
7.1%
O3035
7.1%
p3035
7.1%
t3035
7.1%
c3035
7.1%
Other values (6)7446
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i6070
14.3%
b3865
9.1%
e3865
9.1%
F3035
7.1%
r3035
7.1%
3035
7.1%
O3035
7.1%
p3035
7.1%
t3035
7.1%
c3035
7.1%
Other values (6)7446
17.5%

Avg Monthly GB Download
Real number (ℝ)

High correlation  Zeros 

Distinct50
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.515405
Minimum0
Maximum85
Zeros1526
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:46.113183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median17
Q327
95-th percentile69
Maximum85
Range85
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.41894
Coefficient of variation (CV)0.99529792
Kurtosis0.88150231
Mean20.515405
Median Absolute Deviation (MAD)12
Skewness1.2165839
Sum144490
Variance416.93313
MonotonicityNot monotonic
2025-12-07T17:30:46.229176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01526
 
21.7%
19220
 
3.1%
27199
 
2.8%
30193
 
2.7%
59192
 
2.7%
26191
 
2.7%
23179
 
2.5%
22172
 
2.4%
21171
 
2.4%
18164
 
2.3%
Other values (40)3836
54.5%
ValueCountFrequency (%)
01526
21.7%
2116
 
1.6%
3130
 
1.8%
4129
 
1.8%
5114
 
1.6%
6114
 
1.6%
7116
 
1.6%
8120
 
1.7%
9116
 
1.6%
10132
 
1.9%
ValueCountFrequency (%)
8548
 
0.7%
8243
 
0.6%
7658
 
0.8%
7515
 
0.2%
7381
1.2%
7142
 
0.6%
6975
 
1.1%
59192
2.7%
5845
 
0.6%
5734
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5024 
True
2019 
ValueCountFrequency (%)
False5024
71.3%
True2019
28.7%
2025-12-07T17:30:46.328175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Online Backup
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4614 
True
2429 
ValueCountFrequency (%)
False4614
65.5%
True2429
34.5%
2025-12-07T17:30:46.411175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Device Protection Plan
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4621 
True
2422 
ValueCountFrequency (%)
False4621
65.6%
True2422
34.4%
2025-12-07T17:30:46.492183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4999 
True
2044 
ValueCountFrequency (%)
False4999
71.0%
True2044
29.0%
2025-12-07T17:30:46.572176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Streaming TV
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4336 
True
2707 
ValueCountFrequency (%)
False4336
61.6%
True2707
38.4%
2025-12-07T17:30:46.653176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Streaming Movies
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4311 
True
2732 
ValueCountFrequency (%)
False4311
61.2%
True2732
38.8%
2025-12-07T17:30:46.733176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Streaming Music
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4555 
True
2488 
ValueCountFrequency (%)
False4555
64.7%
True2488
35.3%
2025-12-07T17:30:46.814186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Unlimited Data
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4745 
False
2298 
ValueCountFrequency (%)
True4745
67.4%
False2298
32.6%
2025-12-07T17:30:46.895176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Contract
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.3 KiB
Month-to-Month
3610 
Two Year
1883 
One Year
1550 

Length

Max length14
Median length14
Mean length11.075394
Min length8

Characters and Unicode

Total characters78004
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-Month
2nd rowMonth-to-Month
3rd rowMonth-to-Month
4th rowMonth-to-Month
5th rowMonth-to-Month

Common Values

ValueCountFrequency (%)
Month-to-Month3610
51.3%
Two Year1883
26.7%
One Year1550
22.0%

Length

2025-12-07T17:30:46.995764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T17:30:47.088881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month3610
34.5%
year3433
32.8%
two1883
18.0%
one1550
14.8%

Most occurring characters

ValueCountFrequency (%)
o12713
16.3%
t10830
13.9%
n8770
11.2%
M7220
9.3%
h7220
9.3%
-7220
9.3%
e4983
 
6.4%
3433
 
4.4%
Y3433
 
4.4%
a3433
 
4.4%
Other values (4)8749
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)78004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o12713
16.3%
t10830
13.9%
n8770
11.2%
M7220
9.3%
h7220
9.3%
-7220
9.3%
e4983
 
6.4%
3433
 
4.4%
Y3433
 
4.4%
a3433
 
4.4%
Other values (4)8749
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)78004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o12713
16.3%
t10830
13.9%
n8770
11.2%
M7220
9.3%
h7220
9.3%
-7220
9.3%
e4983
 
6.4%
3433
 
4.4%
Y3433
 
4.4%
a3433
 
4.4%
Other values (4)8749
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)78004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o12713
16.3%
t10830
13.9%
n8770
11.2%
M7220
9.3%
h7220
9.3%
-7220
9.3%
e4983
 
6.4%
3433
 
4.4%
Y3433
 
4.4%
a3433
 
4.4%
Other values (4)8749
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True4171
59.2%
False2872
40.8%
2025-12-07T17:30:47.180035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Payment Method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size428.5 KiB
Bank Withdrawal
3909 
Credit Card
2749 
Mailed Check
 
385

Length

Max length15
Median length15
Mean length13.274741
Min length11

Characters and Unicode

Total characters93494
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBank Withdrawal
2nd rowCredit Card
3rd rowBank Withdrawal
4th rowBank Withdrawal
5th rowBank Withdrawal

Common Values

ValueCountFrequency (%)
Bank Withdrawal3909
55.5%
Credit Card2749
39.0%
Mailed Check385
 
5.5%

Length

2025-12-07T17:30:47.281058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T17:30:47.376058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bank3909
27.8%
withdrawal3909
27.8%
credit2749
19.5%
card2749
19.5%
mailed385
 
2.7%
check385
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a14861
15.9%
d9792
10.5%
r9407
10.1%
7043
 
7.5%
i7043
 
7.5%
t6658
 
7.1%
C5883
 
6.3%
h4294
 
4.6%
k4294
 
4.6%
l4294
 
4.6%
Other values (7)19925
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)93494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a14861
15.9%
d9792
10.5%
r9407
10.1%
7043
 
7.5%
i7043
 
7.5%
t6658
 
7.1%
C5883
 
6.3%
h4294
 
4.6%
k4294
 
4.6%
l4294
 
4.6%
Other values (7)19925
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)93494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a14861
15.9%
d9792
10.5%
r9407
10.1%
7043
 
7.5%
i7043
 
7.5%
t6658
 
7.1%
C5883
 
6.3%
h4294
 
4.6%
k4294
 
4.6%
l4294
 
4.6%
Other values (7)19925
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)93494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a14861
15.9%
d9792
10.5%
r9407
10.1%
7043
 
7.5%
i7043
 
7.5%
t6658
 
7.1%
C5883
 
6.3%
h4294
 
4.6%
k4294
 
4.6%
l4294
 
4.6%
Other values (7)19925
21.3%

Monthly Charge
Real number (ℝ)

High correlation 

Distinct1585
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.761692
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:47.481058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.35
Q389.85
95-th percentile107.4
Maximum118.75
Range100.5
Interquartile range (IQR)54.35

Descriptive statistics

Standard deviation30.090047
Coefficient of variation (CV)0.46462725
Kurtosis-1.2572597
Mean64.761692
Median Absolute Deviation (MAD)24.05
Skewness-0.22052443
Sum456116.6
Variance905.41093
MonotonicityNot monotonic
2025-12-07T17:30:47.600099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0561
 
0.9%
19.8545
 
0.6%
19.9544
 
0.6%
19.944
 
0.6%
19.743
 
0.6%
19.6543
 
0.6%
2043
 
0.6%
20.1540
 
0.6%
19.5540
 
0.6%
19.7539
 
0.6%
Other values (1575)6601
93.7%
ValueCountFrequency (%)
18.251
 
< 0.1%
18.41
 
< 0.1%
18.551
 
< 0.1%
18.72
 
< 0.1%
18.751
 
< 0.1%
18.87
0.1%
18.855
0.1%
18.92
 
< 0.1%
18.956
0.1%
197
0.1%
ValueCountFrequency (%)
118.751
< 0.1%
118.651
< 0.1%
118.62
< 0.1%
118.351
< 0.1%
118.21
< 0.1%
117.81
< 0.1%
117.61
< 0.1%
117.51
< 0.1%
117.451
< 0.1%
117.351
< 0.1%

Total Charges
Real number (ℝ)

High correlation 

Distinct6540
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.3813
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:47.716099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.65
Q1400.15
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8666
Interquartile range (IQR)3386.45

Descriptive statistics

Standard deviation2266.2205
Coefficient of variation (CV)0.99379016
Kurtosis-0.22769266
Mean2280.3813
Median Absolute Deviation (MAD)1219.75
Skewness0.96379109
Sum16060725
Variance5135755.2
MonotonicityNot monotonic
2025-12-07T17:30:47.832139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.211
 
0.2%
19.759
 
0.1%
19.98
 
0.1%
19.658
 
0.1%
20.058
 
0.1%
45.37
 
0.1%
19.557
 
0.1%
20.156
 
0.1%
19.456
 
0.1%
20.256
 
0.1%
Other values (6530)6967
98.9%
ValueCountFrequency (%)
18.81
 
< 0.1%
18.852
< 0.1%
18.91
 
< 0.1%
191
 
< 0.1%
19.051
 
< 0.1%
19.13
< 0.1%
19.151
 
< 0.1%
19.24
0.1%
19.253
< 0.1%
19.34
0.1%
ValueCountFrequency (%)
8684.81
< 0.1%
8672.451
< 0.1%
8670.11
< 0.1%
8594.41
< 0.1%
8564.751
< 0.1%
8547.151
< 0.1%
8543.251
< 0.1%
8529.51
< 0.1%
8496.71
< 0.1%
8477.71
< 0.1%

Total Refunds
Real number (ℝ)

Zeros 

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9621823
Minimum0
Maximum49.79
Zeros6518
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:47.946140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.149
Maximum49.79
Range49.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9026144
Coefficient of variation (CV)4.0274618
Kurtosis18.350658
Mean1.9621823
Median Absolute Deviation (MAD)0
Skewness4.3285167
Sum13819.65
Variance62.451314
MonotonicityNot monotonic
2025-12-07T17:30:48.066139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06518
92.5%
46.062
 
< 0.1%
9.732
 
< 0.1%
32.552
 
< 0.1%
27.62
 
< 0.1%
41.742
 
< 0.1%
12.482
 
< 0.1%
29.762
 
< 0.1%
25.672
 
< 0.1%
29.882
 
< 0.1%
Other values (490)507
 
7.2%
ValueCountFrequency (%)
06518
92.5%
1.011
 
< 0.1%
1.091
 
< 0.1%
1.271
 
< 0.1%
1.312
 
< 0.1%
1.481
 
< 0.1%
1.651
 
< 0.1%
1.661
 
< 0.1%
1.691
 
< 0.1%
1.831
 
< 0.1%
ValueCountFrequency (%)
49.791
< 0.1%
49.761
< 0.1%
49.572
< 0.1%
49.531
< 0.1%
49.511
< 0.1%
49.381
< 0.1%
49.371
< 0.1%
49.241
< 0.1%
49.231
< 0.1%
49.221
< 0.1%

Total Extra Data Charges
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8607128
Minimum0
Maximum150
Zeros6315
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:48.166140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.104978
Coefficient of variation (CV)3.6592376
Kurtosis16.458874
Mean6.8607128
Median Absolute Deviation (MAD)0
Skewness4.0912092
Sum48320
Variance630.25992
MonotonicityNot monotonic
2025-12-07T17:30:48.265141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
06315
89.7%
10138
 
2.0%
4062
 
0.9%
3058
 
0.8%
2051
 
0.7%
8047
 
0.7%
10044
 
0.6%
5043
 
0.6%
15042
 
0.6%
13040
 
0.6%
Other values (6)203
 
2.9%
ValueCountFrequency (%)
06315
89.7%
10138
 
2.0%
2051
 
0.7%
3058
 
0.8%
4062
 
0.9%
5043
 
0.6%
6036
 
0.5%
7034
 
0.5%
8047
 
0.7%
9035
 
0.5%
ValueCountFrequency (%)
15042
0.6%
14038
0.5%
13040
0.6%
12028
0.4%
11032
0.5%
10044
0.6%
9035
0.5%
8047
0.7%
7034
0.5%
6036
0.5%

Total Long Distance Charges
Real number (ℝ)

High correlation  Zeros 

Distinct6087
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.09926
Minimum0
Maximum3564.72
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:48.376155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.545
median401.44
Q31191.1
95-th percentile2577.877
Maximum3564.72
Range3564.72
Interquartile range (IQR)1120.555

Descriptive statistics

Standard deviation846.66005
Coefficient of variation (CV)1.1302375
Kurtosis0.64409208
Mean749.09926
Median Absolute Deviation (MAD)382.12
Skewness1.238282
Sum5275906.1
Variance716833.25
MonotonicityNot monotonic
2025-12-07T17:30:48.497156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0682
 
9.7%
22.864
 
0.1%
15.64
 
0.1%
48.964
 
0.1%
2077.923
 
< 0.1%
177.123
 
< 0.1%
263
 
< 0.1%
597.63
 
< 0.1%
24.483
 
< 0.1%
3783
 
< 0.1%
Other values (6077)6331
89.9%
ValueCountFrequency (%)
0682
9.7%
1.131
 
< 0.1%
1.151
 
< 0.1%
1.171
 
< 0.1%
1.231
 
< 0.1%
1.281
 
< 0.1%
1.471
 
< 0.1%
1.481
 
< 0.1%
1.51
 
< 0.1%
1.591
 
< 0.1%
ValueCountFrequency (%)
3564.721
< 0.1%
35641
< 0.1%
3536.641
< 0.1%
3515.921
< 0.1%
3508.821
< 0.1%
3501.721
< 0.1%
3493.441
< 0.1%
3492.721
< 0.1%
3487.681
< 0.1%
3482.641
< 0.1%

Total Revenue
Real number (ℝ)

High correlation 

Distinct6982
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3034.3791
Minimum21.36
Maximum11979.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-12-07T17:30:48.621194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21.36
5-th percentile78.452
Q1605.61
median2108.64
Q34801.145
95-th percentile8747.041
Maximum11979.34
Range11957.98
Interquartile range (IQR)4195.535

Descriptive statistics

Standard deviation2865.2045
Coefficient of variation (CV)0.9442474
Kurtosis-0.20345739
Mean3034.3791
Median Absolute Deviation (MAD)1767.61
Skewness0.91941027
Sum21371132
Variance8209397.1
MonotonicityNot monotonic
2025-12-07T17:30:48.742194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.273
 
< 0.1%
66.563
 
< 0.1%
68.413
 
< 0.1%
24.83
 
< 0.1%
110.152
 
< 0.1%
4211.62
 
< 0.1%
25.052
 
< 0.1%
1922.72
 
< 0.1%
300.82
 
< 0.1%
5092
 
< 0.1%
Other values (6972)7019
99.7%
ValueCountFrequency (%)
21.361
< 0.1%
21.41
< 0.1%
21.611
< 0.1%
22.081
< 0.1%
22.121
< 0.1%
22.251
< 0.1%
22.281
< 0.1%
22.541
< 0.1%
23.242
< 0.1%
23.281
< 0.1%
ValueCountFrequency (%)
11979.341
< 0.1%
11868.341
< 0.1%
11795.781
< 0.1%
11688.91
< 0.1%
11634.531
< 0.1%
11596.991
< 0.1%
11564.371
< 0.1%
11529.541
< 0.1%
11514.811
< 0.1%
11501.821
< 0.1%

Interactions

2025-12-07T17:30:41.572024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:32.796448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.837670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.788722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.652725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.500818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.452554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.734845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.789174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.713194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.652025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:32.882449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.919669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.893722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.727725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.583832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.557818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.849429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.900174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.805193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.738030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:32.970455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.008626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.978736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.807732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.674824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.668735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.964428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.013174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.902194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.820034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.058456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.116627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.059737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.888724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.760825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.773758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.064172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.105173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.996195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.896025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.140448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.211641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.140731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.966723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.846825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.868756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.162172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.181683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.078194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.974041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.223455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.300627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.232723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.056725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.927928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.970758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.261173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.265684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.159194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:42.048041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.301455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.397628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.312723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.134746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.006863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.064788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.363173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.347683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.237194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:42.130042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.384660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.490628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.398724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.223739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.108925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.164732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.458173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.436683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.318194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:42.222042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.662725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.590641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.484731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.321740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.219918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.271757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.573173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.524683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.405194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:42.314042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:33.752662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:34.684629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:35.571724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:36.415740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:37.341919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:38.383787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:39.680173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:40.621193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-07T17:30:41.490195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-12-07T17:30:48.847195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Avg Monthly GB DownloadAvg Monthly Long Distance ChargesContractDevice Protection PlanInternet ServiceInternet TypeMonthly ChargeMultiple LinesNumber of ReferralsOfferOnline BackupOnline SecurityPaperless BillingPayment MethodPhone ServicePremium Tech SupportReferred a FriendStreaming MoviesStreaming MusicStreaming TVTenure in MonthsTotal ChargesTotal Extra Data ChargesTotal Long Distance ChargesTotal RefundsTotal RevenueUnlimited Data
Avg Monthly GB Download1.000-0.0490.0940.2930.7390.0480.4980.1610.0370.0310.2970.2800.2230.1290.1270.2740.0660.3100.3450.3030.0530.2950.132-0.0260.0170.2170.555
Avg Monthly Long Distance Charges-0.0491.0000.0260.0430.1110.2220.1410.2070.0080.0160.0350.0570.0000.0230.7180.0720.0000.0000.0030.0140.0140.059-0.0090.651-0.0130.2090.082
Contract0.0940.0261.0000.2270.2020.0950.2280.1210.2210.5040.1690.2360.1500.1170.0000.2730.2730.1250.0880.1160.4970.3460.0410.3210.0390.3600.140
Device Protection Plan0.2930.0430.2271.0000.3800.0000.5190.2000.1270.3630.3030.2750.1030.0790.0700.3330.1530.4020.3490.3900.3590.5220.0760.2100.0000.4710.296
Internet Service0.7390.1110.2020.3801.0001.0000.9670.2100.0420.0700.3810.3330.3200.2730.1710.3360.0000.4180.3880.4150.0210.4280.1550.0450.0060.3100.755
Internet Type0.0480.2220.0950.0001.0001.0000.5580.3310.0450.0200.0000.2110.2310.1560.4150.2020.0000.1540.0800.1640.0020.1910.0000.1210.0000.1670.000
Monthly Charge0.4980.1410.2280.5190.9670.5581.0000.5260.0800.2020.4880.4250.3600.2210.6640.4450.1520.6650.5530.6680.2760.6380.1230.3170.0370.5690.729
Multiple Lines0.1610.2070.1210.2000.2100.3310.5261.0000.0750.3480.2020.0970.1630.1520.2790.1000.1320.2580.1930.2570.3330.4690.0610.3330.0460.4610.159
Number of Referrals0.0370.0080.2210.1270.0420.0450.0800.0751.0000.1780.1130.1460.0530.0540.0000.1200.7180.0560.0530.0710.3830.327-0.0250.2570.0370.3390.000
Offer0.0310.0160.5040.3630.0700.0200.2020.3480.1781.0000.3680.3410.0000.0970.0210.3340.3670.2850.2370.2850.9320.5700.0480.4240.0270.5720.049
Online Backup0.2970.0350.1690.3030.3810.0000.4880.2020.1130.3681.0000.2830.1260.0950.0500.2940.1420.2740.2450.2820.3580.5090.1000.2430.0000.4720.283
Online Security0.2800.0570.2360.2750.3330.2110.4250.0970.1460.3410.2831.0000.0000.0440.0920.3540.1390.1870.1950.1750.3260.4200.0570.1980.0310.3830.264
Paperless Billing0.2230.0000.1500.1030.3200.2310.3600.1630.0530.0000.1260.0001.0000.1850.0110.0360.0000.2110.1660.2230.0000.1580.0430.0190.0000.1330.245
Payment Method0.1290.0230.1170.0790.2730.1560.2210.1520.0540.0970.0950.0440.1851.0000.0240.0490.0580.1780.1340.1810.0950.1160.0320.0720.0140.1020.197
Phone Service0.1270.7180.0000.0700.1710.4150.6640.2790.0000.0210.0500.0920.0110.0241.0000.0950.0090.0300.0370.0190.0000.1510.0350.3410.0000.1850.121
Premium Tech Support0.2740.0720.2730.3330.3360.2020.4450.1000.1200.3340.2940.3540.0360.0490.0951.0000.1210.2790.2760.2780.3250.4370.0950.1880.0300.3940.251
Referred a Friend0.0660.0000.2730.1530.0000.0000.1520.1320.7180.3670.1420.1390.0000.0580.0090.1211.0000.1140.0890.1190.3590.3110.0000.2570.0280.3180.014
Streaming Movies0.3100.0000.1250.4020.4180.1540.6650.2580.0560.2850.2740.1870.2110.1780.0300.2790.1141.0000.8480.5330.2830.5180.0940.1870.0000.4630.318
Streaming Music0.3450.0030.0880.3490.3880.0800.5530.1930.0530.2370.2450.1950.1660.1340.0370.2760.0890.8481.0000.4550.2340.4400.0770.1490.0000.3920.296
Streaming TV0.3030.0140.1160.3900.4150.1640.6680.2570.0710.2850.2820.1750.2230.1810.0190.2780.1190.5330.4551.0000.2770.5120.0710.1840.0000.4590.323
Tenure in Months0.0530.0140.4970.3590.0210.0020.2760.3330.3830.9320.3580.3260.0000.0950.0000.3250.3590.2830.2340.2771.0000.8890.0190.6630.0840.9130.006
Total Charges0.2950.0590.3460.5220.4280.1910.6380.4690.3270.5700.5090.4200.1580.1160.1510.4370.3110.5180.4400.5120.8891.0000.0780.6500.0870.9780.328
Total Extra Data Charges0.132-0.0090.0410.0760.1550.0000.1230.061-0.0250.0480.1000.0570.0430.0320.0350.0950.0000.0940.0770.0710.0190.0781.000-0.0040.0090.0670.433
Total Long Distance Charges-0.0260.6510.3210.2100.0450.1210.3170.3330.2570.4240.2430.1980.0190.0720.3410.1880.2570.1870.1490.1840.6630.650-0.0041.0000.0610.7780.029
Total Refunds0.017-0.0130.0390.0000.0060.0000.0370.0460.0370.0270.0000.0310.0000.0140.0000.0300.0280.0000.0000.0000.0840.0870.0090.0611.0000.0820.024
Total Revenue0.2170.2090.3600.4710.3100.1670.5690.4610.3390.5720.4720.3830.1330.1020.1850.3940.3180.4630.3920.4590.9130.9780.0670.7780.0821.0000.235
Unlimited Data0.5550.0820.1400.2960.7550.0000.7290.1590.0000.0490.2830.2640.2450.1970.1210.2510.0140.3180.2960.3230.0060.3280.4330.0290.0240.2351.000

Missing values

2025-12-07T17:30:42.480047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T17:30:42.823050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-07T17:30:43.012131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer IDCountQuarterReferred a FriendNumber of ReferralsTenure in MonthsOfferPhone ServiceAvg Monthly Long Distance ChargesMultiple LinesInternet ServiceInternet TypeAvg Monthly GB DownloadOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodMonthly ChargeTotal ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesTotal Revenue
08779-QRDMV1Q3No01NaNNo0.00NoYesDSL8NoNoYesNoNoYesNoNoMonth-to-MonthYesBank Withdrawal39.6539.650.00200.0059.65
17495-OOKFY1Q3Yes18Offer EYes48.85YesYesFiber Optic17NoYesNoNoNoNoNoYesMonth-to-MonthYesCredit Card80.65633.300.000390.801024.10
21658-BYGOY1Q3No018Offer DYes11.33YesYesFiber Optic52NoNoNoNoYesYesYesYesMonth-to-MonthYesBank Withdrawal95.451752.5545.610203.941910.88
34598-XLKNJ1Q3Yes125Offer CYes19.76NoYesFiber Optic12NoYesYesNoYesYesNoYesMonth-to-MonthYesBank Withdrawal98.502514.5013.430494.002995.07
44846-WHAFZ1Q3Yes137Offer CYes6.33YesYesFiber Optic14NoNoNoNoNoNoNoYesMonth-to-MonthYesBank Withdrawal76.502868.150.000234.213102.36
54412-YLTKF1Q3No027Offer CYes3.33YesYesFiber Optic18NoNoYesNoNoNoNoNoMonth-to-MonthYesBank Withdrawal78.052135.500.001089.912235.41
60390-DCFDQ1Q3Yes11Offer EYes15.28NoYesFiber Optic30NoNoNoNoNoNoNoYesMonth-to-MonthYesMailed Check70.4570.450.00015.2885.73
73445-HXXGF1Q3Yes658Offer BNo0.00NoYesDSL24NoYesYesNoNoYesNoYesMonth-to-MonthYesBank Withdrawal45.302651.2040.9500.002610.25
82656-FMOKZ1Q3No015Offer DYes44.07YesYesFiber Optic19NoNoNoNoNoNoNoYesMonth-to-MonthYesMailed Check74.451145.700.000661.051806.75
92070-FNEXE1Q3No07Offer EYes26.95NoYesFiber Optic18YesNoNoNoNoNoNoNoMonth-to-MonthNoBank Withdrawal76.45503.6011.050188.65681.20
Customer IDCountQuarterReferred a FriendNumber of ReferralsTenure in MonthsOfferPhone ServiceAvg Monthly Long Distance ChargesMultiple LinesInternet ServiceInternet TypeAvg Monthly GB DownloadOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodMonthly ChargeTotal ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesTotal Revenue
70339281-CEDRU1Q3Yes268NaNYes8.62NoYesDSL53NoYesNoYesYesNoNoYesTwo YearNoBank Withdrawal64.104326.2519.120586.164893.29
70340871-OPBXW1Q3No02Offer EYes6.85NoNoNaN0NoNoNoNoNoNoNoNoMonth-to-MonthYesMailed Check20.0539.250.00013.7052.95
70359767-FFLEM1Q3No038NaNYes35.04NoYesFiber Optic2NoNoNoNoNoNoNoYesMonth-to-MonthYesCredit Card69.502625.2520.1901331.523936.58
70368456-QDAVC1Q3No019NaNYes29.55NoYesFiber Optic13NoNoNoNoYesNoNoYesMonth-to-MonthYesBank Withdrawal78.701495.1026.840561.452029.71
70377750-EYXWZ1Q3No012NaNNo0.00NoYesCable24NoYesYesYesYesYesYesYesOne YearNoBank Withdrawal60.65743.3040.4100.00702.89
70382569-WGERO1Q3No072NaNYes22.77NoNoNaN0NoNoNoNoNoNoNoNoTwo YearYesBank Withdrawal21.151419.4019.3101639.443039.53
70396840-RESVB1Q3Yes124Offer CYes36.05YesYesCable24YesNoYesYesYesYesYesYesOne YearYesMailed Check84.801990.5048.230865.202807.47
70402234-XADUH1Q3Yes472NaNYes29.66YesYesFiber Optic59NoYesYesNoYesYesYesYesOne YearYesCredit Card103.207362.9045.3802135.529453.04
70414801-JZAZL1Q3Yes111NaNNo0.00NoYesDSL17YesNoNoNoNoNoNoYesMonth-to-MonthYesBank Withdrawal29.60346.4527.2400.00319.21
70423186-AJIEK1Q3No066NaNYes30.96NoYesFiber Optic11YesNoYesYesYesYesYesYesTwo YearYesBank Withdrawal105.656844.500.0002043.368887.86